A mathematical model of finding usability problems is presented, based on data from 11 studies. The model uses a Poisson process to describe the detection of usability problems as a function of the number of users tested or heuristic evaluators employed. The model can be used to plan the amount of evaluation required to achieve desired levels of thoroughness or benefits. Results of early tests can provide estimates of the number of problems left to be found and the number of additional evaluations needed to find a given fraction. With quantitative evaluation costs and detection values, the model can estimate the numbers of evaluations at which optimal cost/benefit ratios are obtained and at which marginal utility vanishes. User testing and heuristic evaluation are both debugging methods in the usability engineering lifecycle. They aim to find and document as many usability problems in a user interface design as possible. User testing involves observing real users interacting with a system, while heuristic evaluation involves usability specialists judging a user interface based on established principles. Both methods have advantages and disadvantages, but they share similarities in that they are both debugging methods and involve aggregating results from multiple smaller evaluations. The model assumes that the finding of usability problems follows a Poisson process, where the probability of finding a problem is independent of previous tests. This assumption is supported by the fact that the Poisson model has been found to describe the finding of traditional programming errors in software development projects. The model can be used to estimate the number of usability problems in an interface and the number of evaluations needed to find them. The model also indicates the likely number of problems that will be found by the next test subject or heuristic evaluator. The model can be used to estimate the optimal number of evaluators or test users needed to achieve the desired level of thoroughness. The model assumes that the probability of finding a usability problem is independent of whether it has been found before and independent of each other. The model can be used to estimate the number of usability problems in an interface and the number of evaluations needed to find them. The model also indicates the likely number of problems that will be found by the next test subject or heuristic evaluator. The model can be used to estimate the optimal number of evaluators or test users needed to achieve the desired level of thoroughness. The model can be used to estimate the number of usability problems in an interface and the number of evaluations needed to find them. The model also indicates the likely number of problems that will be found by the next test subject or heuristic evaluator.A mathematical model of finding usability problems is presented, based on data from 11 studies. The model uses a Poisson process to describe the detection of usability problems as a function of the number of users tested or heuristic evaluators employed. The model can be used to plan the amount of evaluation required to achieve desired levels of thoroughness or benefits. Results of early tests can provide estimates of the number of problems left to be found and the number of additional evaluations needed to find a given fraction. With quantitative evaluation costs and detection values, the model can estimate the numbers of evaluations at which optimal cost/benefit ratios are obtained and at which marginal utility vanishes. User testing and heuristic evaluation are both debugging methods in the usability engineering lifecycle. They aim to find and document as many usability problems in a user interface design as possible. User testing involves observing real users interacting with a system, while heuristic evaluation involves usability specialists judging a user interface based on established principles. Both methods have advantages and disadvantages, but they share similarities in that they are both debugging methods and involve aggregating results from multiple smaller evaluations. The model assumes that the finding of usability problems follows a Poisson process, where the probability of finding a problem is independent of previous tests. This assumption is supported by the fact that the Poisson model has been found to describe the finding of traditional programming errors in software development projects. The model can be used to estimate the number of usability problems in an interface and the number of evaluations needed to find them. The model also indicates the likely number of problems that will be found by the next test subject or heuristic evaluator. The model can be used to estimate the optimal number of evaluators or test users needed to achieve the desired level of thoroughness. The model assumes that the probability of finding a usability problem is independent of whether it has been found before and independent of each other. The model can be used to estimate the number of usability problems in an interface and the number of evaluations needed to find them. The model also indicates the likely number of problems that will be found by the next test subject or heuristic evaluator. The model can be used to estimate the optimal number of evaluators or test users needed to achieve the desired level of thoroughness. The model can be used to estimate the number of usability problems in an interface and the number of evaluations needed to find them. The model also indicates the likely number of problems that will be found by the next test subject or heuristic evaluator.